Knowledge distillation has emerged as a critical technique for enabling efficient knowledge transfer between artificial intelligence agents, particularly in resource-constrained and distributed environments. At its core, knowledge distillation is a model compression method where a smaller "student" model learns to replicate the behavior and decision-making patterns of a larger, more complex "teacher" model. In multi-agent systems, this paradigm extends beyond simple compression to facilitate cross-agent knowledge sharing, enabling heterogeneous agents with different architectures and capabilities to collaborate effectively while maintaining computational efficiency.
The fundamental mechanism of knowledge distillation involves training student models on "soft targets"—probability distributions produced by teacher models—rather than hard ground-truth labels. These soft targets, processed through temperature-scaled softmax functions, reveal nuanced information about class relationships and decision boundaries that ground-truth labels cannot capture. This "dark knowledge" enables more effective learning and generalization, particularly when transferring policies and decision-making strategies across agents in multi-agent reinforcement learning (MARL) environments.
Cross-agent knowledge transfer in multi-agent systems employs several sophisticated mechanisms to enable effective coordination and learning:
Soft target transfer forms the foundation, where teacher agents generate probability distributions over actions or predictions using temperature scaling (typically T=1-20), softening the output to reveal subtle relationships between different choices. The distillation loss, calculated as the Kullback-Leibler divergence between teacher and student distributions, guides the student agent to learn not just what to predict but how the teacher makes those predictions.
Feature-based knowledge transfer has gained significant attention as it enables distillation of intermediate representations rather than just final outputs. Multi-layer attention maps extract features between teacher and student networks, building knowledge transfer mechanisms that capture spatial correlations and internal reasoning patterns. However, a critical challenge remains: the dimension gap between teacher and student intermediate features often requires careful architectural design or learned projection functions to align heterogeneous representations.
Policy distillation specifically addresses reinforcement learning scenarios, enabling the extraction of expert policies from complex agents and consolidation into more efficient networks suitable for decentralized execution. Recent frameworks like FedHPD (Heterogeneous Federated Reinforcement Learning via Policy Distillation) use action probability distributions as the medium for knowledge sharing among heterogeneous agents in black-box settings where internal network details are not disclosed. This approach has demonstrated significant improvements in sample efficiency—up to 18.8% in some multi-agent benchmarks—while maintaining competitive performance.
Policy distillation has become indispensable for deploying multi-agent systems in resource-constrained environments such as drone swarms, IoT networks, and connected autonomous vehicles:
The CTPDE (Centralized Training and Policy Distillation for Decentralized Execution) framework exemplifies recent advances, leveraging centralized training to create powerful teacher policies that are then distilled into lightweight agents for efficient decentralized execution. This approach addresses the fundamental challenge of balancing computational efficiency with decision-making effectiveness in edge computing scenarios.
The DDN, proposed in February 2025, introduces a dual-module architecture consisting of an External Distillation Module that reduces accumulated inherent errors through distillation learning, and an Internal Distillation Module that leverages global state information to generate environment-related intrinsic rewards. This isolation design effectively eliminates errors caused by value function decomposition while improving training efficiency of collaborative policies in cooperative multi-agent settings.
The KG-MASD formulates distillation as a Markov Decision Process, incorporating knowledge graphs as verifiable structured priors to transfer collaborative reasoning capabilities from multi-agent large language models to lightweight student models. This approach addresses the challenge of distilling not just individual agent behaviors but the emergent reasoning that arises from agent collaboration.
The 2024-2025 period has witnessed significant innovations in cross-agent knowledge distillation techniques:
Presented at NeurIPS 2024, RCD-KD introduces a reinforcement learning-based module that dynamically selects suitable target domain samples for knowledge transfer based on the student network's capabilities. Unlike traditional approaches that coarsely align all samples, RCD-KD adaptively matches knowledge transfer to student capacity, employing a domain discriminator to transfer domain-invariant knowledge across different operating environments.
Cross-task knowledge distillation enables knowledge transfer between agents trained on entirely different tasks, breaking the traditional constraint that teacher and student must address the same problem. The Prototype-guided Cross-task Knowledge Distillation (ProC-KD) method migrates intrinsic local-level object knowledge from teacher networks to various task scenarios, alleviating constraints imposed by different label spaces. Research demonstrates up to 1.9% improvement in cross-task settings through the use of inverted projections to address representational limitations.
In federated learning contexts, several frameworks have emerged to enable knowledge distillation across distributed heterogeneous agents. FedGPD (Global Prototype Distillation) uses global class prototypes as knowledge to instruct local training on client devices, achieving 0.22-1.28% accuracy improvements over previous state-of-the-art methods on benchmark datasets. FedUKD (Unsupervised Federated Learning with Bilateral Knowledge Distillation) employs bilateral knowledge distillation to enable mutual knowledge transfer between nodes under unlabeled data conditions while integrating a Domain Bias Eliminator framework to reduce model bias from data heterogeneity.
The TAPD framework, introduced in December 2024, incorporates a task-agnostic exploration phase where agents explore environments without external goals while maximizing intrinsic motivation. Distillation of this intrinsically motivated behavior serves as a strong regularizer, enabling higher final performance and improved sample efficiency when agents subsequently tackle specific tasks.
Knowledge distillation has proven transformative for agent training efficiency across multiple domains:
In offline MARL, where agents must learn from previously collected data without additional online collection, distillation enables training teacher policies as if the dataset were generated by a single agent, then creating separate student policies that learn both feature values and structural relations among different agents. This approach addresses the challenge of credit assignment in cooperative settings while maintaining computational tractability.
For CAVs, multi-agent policy distillation enables cooperative decision-making that improves transportation system efficiency and safety. Language-driven policy distillation frameworks have emerged that incorporate natural language instructions to guide coordination patterns, enabling more interpretable and adaptable agent behaviors in complex traffic scenarios.
In industrial applications, knowledge distillation facilitates information sharing for online process monitoring in decentralized manufacturing systems. Agents monitoring different production stages can distill their local expertise into shared representations that enable system-wide optimization while preserving proprietary information and reducing communication overhead—FedKD demonstrates up to 94.89% reduction in communication costs while maintaining competitive performance with centralized learning.
The IoA paradigm leverages knowledge distillation for multi-domain applications such as IoT resource scheduling. The KD-AFRL framework addresses heterogeneity in resource characteristics and workload patterns through adaptive knowledge distillation that accounts for domain-specific constraints and varying computational capabilities. Agents compress and contextualize messages by transmitting only updates relative to previously shared knowledge, enabling meaning-aware communication rather than raw data exchange.
Despite significant advances, cross-agent knowledge distillation faces substantial challenges when dealing with heterogeneous agents and large-scale deployments:
Scalability in asynchronous settings introduces unique challenges when integrating knowledge distillation into asynchronous federated learning (AFL). Stale client models may serve as poor or inconsistent teachers, and the distillation process must remain lightweight to preserve AFL's scalability advantage. The inconsistent learning progress resulting from different agent configurations increases complexity in federated optimization.
The future of cross-agent knowledge distillation points toward several promising research directions:
Adaptive coordination mechanisms will focus on designing dynamic systems that vary communication verbosity across task phases, supporting both breadth-depth exploration and efficient knowledge sharing. These mechanisms must balance the trade-off between comprehensive information exchange during learning and minimal communication during deployment.
Cross-domain knowledge synthesis represents a frontier where multi-agent systems function as domain-specific experts collaborating to provide comprehensive insights transcending single-domain expertise. Advanced coordination mechanisms will manage interactions among agents with diverse specializations, ensuring system scalability while maintaining coherent knowledge integration.
Continual and lifelong distillation will enable agents to accumulate knowledge over extended operational periods, distilling experiences from multiple tasks and environments into continuously improving policies. This requires addressing catastrophic forgetting while maintaining plasticity for new learning, potentially through progressive distillation architectures that incrementally compress and transfer knowledge as agents encounter new situations.
Interoperability protocols such as Google's Agent-to-Agent (A2A) protocol will facilitate seamless communication among heterogeneous AI agents from different developers and platforms. Standardized distillation interfaces will enable agents to share knowledge despite architectural differences, democratizing access to expertise and enabling emergent collaborative capabilities.
Self-distillation and mutual distillation paradigms will move beyond traditional teacher-student hierarchies toward peer learning frameworks where agents of similar capabilities engage in bidirectional knowledge exchange. Online distillation methods enable simultaneous training of multiple agents that teach each other, potentially accelerating learning and improving robustness through diverse perspectives.